Improve driver performance by experience of driver cognitive behavior model's practice

Driver profiling – Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation

Origins and evolution of enactive cognitive science: Toward an enactive cognitive architecture

In this paper we investigate driver behavior variables proposed in literature from an objective approach; we consider studies which employed numeric data that can be objectively measured by standardized tools and electronic devices, such as speed, acceleration and position. Our analysis develops in three phases; first we identify recent fields that focused on modeling driver behavior, such as Usage based insurance and governement agencies of transport. Next we extract quantitative variables from literature and classify them according to their importance; to achieve this, we identify their priority in each paper depending on their results. Finally we order them by their rate use in scientific literature, in order to have a comprehensive synthesis about the most significant and prevalent variables in literature. This paper therefore serves as reference for future studies on driver behavior; the collaboration we provide is a set of crucial variables for modeling, understanding and analysis driver behavior in quantitative research. Results show that “Speed”, “Acceleration/deceleration” and “Braking” are the most used in recent studies as well as considered priority variables.

Cognitive Architectures are used to test theoretical and conceptual frameworks identifying and explaining the underlying components of thought, namely the essential structures, mechanisms, and processes realized through models of human-like information processing. They define and prescribe those boundaries deemed both necessary and sufficient for intelligent agents based on our current understanding of human cognition. The Common Model of Cognition (CMC) attempts to establish a community consensus on theoretical commitments and assumptions built into commonly used architectures, and group the assumptions according to structure and processing, memory, learning, and perceptual interfaces. The CMC remains a verbal-conceptual consensus across broad theories essential for general phenomenon (i.e., a Meta-Model of Cognition), however, lacks a formal specification to support domain-general task model comparisons for evaluation and validation of new theories implemented in new or existing architectures, or specific micro-theoretic implementations as cognitive architecture models and task models. Thus, a lack of a formal model supporting the CMC inhibits exploration of philosophical enquiries iii about core theoretical assumptions, and the adoption of refined peripheral theories across architectures. This thesis presents a formal meta-model specific to the constraints represented at Newell's Cognitive level using the principles of Model-Driven Design (MDD) to encapsulate these entities and relationships across architectures. This formal model can be used as a framework generator, and to specify the abstract interfaces common across Common Model agents, allowing modelers to explore verbal-conceptual theories through experimentation with virtual environments, further supporting a common ground. Frameworks generated through MDD support an empirical evaluation and comparison of variations on the Common Model for the purpose of application to Artificial Intelligence problem domains, lending additional credibility to computational cognitive modeling as a formal discipline, and the Cognitive Science research enterprise as a whole.

Cognitive and behavioral models of assertive behavior: Review, analysis and integration

Understanding of mechanisms and factors responsible for the driver behavior on the road is the subject of ongoing interest to transportation psychologists, occupational doctors and engineers. Models of driver behavior are a key point for the understanding the mechanisms and factors which may cause limitations to the optimal functioning on the road. They also systematize knowledge about the factors responsible for the behavior of the driver and thus constitute a starting point for formulating empirical or diagnostic hypotheses. The aim of this study is to present models of driver behavior from the descriptive and functional perspectives. Med Pr 2017;68(3):401-411.

In a series of four studies, the aim of the current research project was to evaluate the cognitive-behavioral theory and treatment (CBT) of eating disorders. The first study (Chapter 5) was a meta-analysis (Linardon, Wade, De la Piedad Garcia, & Brennan, in press) of randomized controlled trials (RCT) testing the efficacy of CBT for eating disorders. Pooling data from 79 RCTs, results showed that therapist-led and guided self-help CBT were efficacious for individuals with bulimia nervosa (BN) and binge eating disorder (BED). There was no evidence to suggest that CBT was more efficacious than other psychological interventions in anorexia nervosa (AN). Having found evidence supporting the efficacy of CBT for certain eating disorder presentations, the second study (Chapter 6), which was a systematic review, focused on identifying the reliable factors that mediate, moderate, or predict outcome during CBT (Linardon, de la Piedad Garcia, & Brennan, 2016b). This review found that mediators and moderators of change have been largely unexplored, and that no reliable predictors of outcome emerged. Therefore, based on existing evidence, it was concluded that it remains unclear how, for whom, and under what conditions, CBT for eating disorders works. To understand the mechanisms through which CBT for eating disorders may work, a cross-sectional evaluation of the cognitive-behavioral model in a large non-clinical sample was employed for the third study (Chapter 8; under review). This study validated the conceptual pathways hypothesized by the cognitive model; it also identified two additional variables that might be important mechanisms of change during CBT, body checking and dichotomous thinking. The inclusion of body checking and dichotomous thinking within the cognitive-behavioral model explained nearly three times the amount of variance in disordered eating symptoms than the model without these variables. The third study offered the necessary statistical support for the cognitive-behavioral model and its hypotheses. The final study (Chapter 10) utilized a single case experimental design (n=8) to test the hypothesized cognitive-behavioral mechanisms of change during a CBT guided self-help program. The potential mechanisms of change examined were shape and weight concerns, dietary restraint, and adherence to regular eating strategies. Preliminary evidence showed that an adherence to regular eating in the second week of CBT was associated with a concurrent decrease in dietary restraint and binge eating. From this study, there was no evidence that other potential mechanisms were operating to reduce binge eating behavior. Based on these four independent research studies, this thesis gathered converging evidence in support for the underling cognitive-behavioral model of eating disorders. Further, these findings suggest that CBT for eating disorders is likely to contain several theory-specific mechanisms that are responsible for this treatments effectiveness. More broadly, the current thesis offers support for the contention that CBT for eating disorders “works” because of the reason outlined by its underlying model and because of its specific therapeutic mechanisms.

Risky driving is a leading cause of traffic accidents all over the world. Major factors contributing to risky driving include driver distraction (e.g. smart-phone usage) and an onset of driver drowsiness. As a result, improving road-safety requires modeling and detecting in-vehicle conditions indicative of risky driver behavior. Previous works towards driver behavior modeling and classification possess limitations such as increased expenditure and/or additional external infrastructure requirements. In this paper, we propose a novel driver behavior detection framework that at its core makes use of in-vehicle Wi-Fi signals. The main premise of the proposed framework is that risky behavior induces specific differentiable patterns in received WiFi signals - patterns that are then exploited to build an efficient classification system. More specifically, we classify drowsy and inattentive driving into four main gestures that reflect unsafe driving. These gestures include (a) Yawning, (b) Head Jerks, (c) Sideways motion, and (d) Smart-phone usage. Channel State Information (CSI) is used to determine the driver's gesture which is further used for behavior modeling. A number of low computational features are used to classify among different gestures. Experiments are performed in a real in-vehicle environment using software-defined radios. Using data collected from these experiments, a simple ensemble classifier is implemented which is shown to achieve an average accuracy of 88.64%.

In 2020, Mc Fadden published an article in which he discusses how algorithms can be encoded in time and space. By analyzing the topology of the cytoarchitecture of the brain, cognitive architectures can understand the underlying mechanisms that have led to the development of human intelligence in space. In this study, our focus lies in investigating temporal heterogeneity as a mechanism that the brain could have developed not solely as a biological constraint, but also as an evolutionary advantage. To accomplish this, we employed virtual agents within a virtual environment and constructed a prototype cognitive architecture. Subsequently, we compared the benefits and drawbacks of having this cognitive architecture operate under a model of temporal heterogeneity versus one characterized by temporal homogeneity. At the conclusion of the article, we present the results obtained from two perspectives. From a quantitative standpoint, we contrast the agents’ adaptation to the environment based on the cognitive architecture model employed by each agent. On this front, we found evidence that temporal heterogeneity might be useful in finding parameter optimizations faster, amongst other benefits. From a qualitative perspective, we examine the potential of this model to explore the cognitive processes of the virtual agents, concluding that a different representation of percepts is needed, which we further discuss.

Objective Before market introduction, the safety of highly automated driving systems needs to be assessed prospectively. BMW has developed a holistic approach for the assessment of the traffic safety impact by these systems in which stochastic traffic simulations play a significant role. A driver behavior model which represents realistic driver behavior ranging from performance in non-critical everyday driving toward performance in critical situations is key for this approach. To ensure trustworthy results, validation of the driver model is needed. The paper aims at demonstrating that the presented driver model acts realistically in different critical real-world traffic scenarios. Methods BMW has been developing the Stochastic Cognitive Model (SCM) which models cognitive processes in traffic situations. These processes range from information acquisition by gaze behavior, mental representation of the environment, recognition of situations from the visual information and reaction to the situation. The driver model combines these cognitive processes with stochastic driver parameters to obtain a variation in driver behavior in simulations. Especially visual attention modeling is key to realistic traffic interactions in simulations as this is the input for the sequential cognitive processes, i.e., the recognition of situations and the reaction to the situation. Modeling of driver’s gaze behavior with SCM is thus shown in this paper. Results SCM is applied in three critical real-world traffic scenarios in which gaze behavior, brake reaction times and time-to-collisions are evaluated and compared to the real-world data. Due to the stochastic approach not only a single SCM agent but a collective of virtual SCM test drivers is assessed. Results show that SCM is capable to simulate the influence of visual inattention on collision risk. Conclusion Realistic driver behavior in simulations can be achieved by using SCM. Especially in the presented critical scenarios SCM is able to represent real-world driving behavior which is determined particularly by its gaze behavior and subsequent reaction. Driving performance varies over different SCM agents which mean that different driving behavior can be simulated with SCM as well. However, the investigation in this paper included only three real-world cases. Therefore, further critical, and additionally non-critical scenarios need to be investigated in the future.

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.

In order to enhance the traffic road safety, applying the artificial intelligence theory and the technology in road traffic control system, that is intelligent driving vehicle, may effectively decrease traffic accidents, which are caused by drivers' neglect, fatigue and so on. This article is based on cognitive science to analyze driving behaviors. First, we estimated the Information Processing Model of Intelligent Vehicle. Second, based on prototype pattern recognition theory, we also built Pattern Recognition Model of Intelligent Driving Behavior, which provided the advantageous basis for the further research on intelligent vehicle. Third, virtual scene simulation system was established by using Creator and Vega Prime. Finally, according to test data, we investigated the features of speed and latitudinal distance in both artificial and intelligent U-Turn driving behavior. Our results describe that the intelligent vehicle does have the ability of cognizance, and it can make correct reactions corresponding to the driving environment, but its intelligence still needs improving.

Multi-scale driver behavior modeling based on deep spatial-temporal representation for intelligent vehicles

Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

The term 'branching' refers to processes needed for successful reuptake of a task after interruption by another task. Based on a model of human prefrontal cognitive architecture, it has been postulated that people cannot branch recursively between more than two tasks due to a capacity limit built into the cognitive architecture (Koechlin and Hyafil in Science 318:594-598, 2007). As an alternative to a structural limit for recursive branching between more than two tasks we put forward the hypothesis that working memory capacity is the limiting factor in recursive branching. We tested this hypothesis by independently varying working memory load and number of recursive branching steps. Successful branching between up to four tasks was observed, as long as working memory load was kept low. Our data, thus, do not support the proposition of a structural limit to recursive branching beyond two tasks. Instead, they suggest that working memory capacity limit is the most important factor that limits the capacity for branching. We further observed that the requirement to retain task sets and task contents additively contributed to the difficulty of recursive branching. In a broader context, our data thus support working memory models that conceptualize working memory and executive functions not as separate modules, but as tightly interactive processes.